Adaptive spectral unmixing for histopathology fluorescent images

Accurate spectral unmixing of fluorescent images is clinically important because it is one of the key steps in multiplex histopathology image analysis. The narrow-band reference spectra for quantum dot biomarkers are often precisely known apriori, while the broad-band DAPI (nuclear biomarker) and tissue auto-fluorescence reference spectra are tissue dependent and vary from image to image. This paper presents a novel spectral unmixing algorithm based on data adaptive broad-band reference spectrum refinement for accurate reference spectra estimation of each image. The algorithm detects nuclear and tissue regions from the DAPI channel in the unmixed images, and estimates the new reference spectra for the biomarkers. A nuclear ranking algorithm is proposed for nuclear region segmentation to achieve more robust and accurate reference spectra estimations for the given image. The proposed framework iteratively updates the broad-band reference spectra and unmixes the fluorescent image till convergence. The algorithm was tested on a clinical data set containing a large number of multiplex fluorescent slides and demonstrates better unmixing results than the existing spectral unmixing strategies.